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標題: | 違約預測模型中變數選擇方法之應用 Variable Selection Methods for Default Prediction Model |
作者: | Hsuan Fu 傅萱 |
指導教授: | 管中閔 |
關鍵字: | 維度精簡,破產預測,最佳子集篩選,脊迴歸,逐步回歸,逐段回歸,最小絕對壓縮挑選機制,最小角度迴歸,主成分迴歸,偏最小平方法,考克斯比例風險模型, dimension reduction,bankruptcy prediction,best subset selection,ridge regression,stepwise regression,stagewise regression,lasso,least angle regressions,principle component regression,partial least squares,Cox proportional hazard model., |
出版年 : | 2012 |
學位: | 碩士 |
摘要: | For discovering the useful variables in predicting the occurrence of firm’s default event, we examine 8 variable selection methods, including best subset selection, stepwise regression, stagewise regression, ridge regression (RR), the lasso, least angle regressions (LAR), principle component regression (PCR), and partial least squares regression (PLS). According to our simulation results, the stagewise regression, the lasso, and LAR, among all the variable selection methods, have stable in-sample fitting ability and robust performance in terms of root mean square error (RMSE). As a representative of the other 5 methods, PCR has similar result as the best 3 methods in our empirical analysis. Nevertheless, this thesis recommends the best 3 methods since the computation is less time-consuming and the results are more intuitive to interpret. Moreover, we find the combination of selected variables is time-dependent. Therefore, the incorporation of the frailty factor is inevitable to construct the default prediction model in the future studies. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63911 |
全文授權: | 有償授權 |
顯示於系所單位: | 財務金融學系 |
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